• Corpus ID: 204751965

Metaheuristic macro scale traffic flow optimisation from urban movement data

@article{Arp2020MetaheuristicMS,
  title={Metaheuristic macro scale traffic flow optimisation from urban movement data},
  author={Laurens Arp and Dyon van Vreumingen and Daniela Gawehns and Mitra Baratchi},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.02214}
}
How can urban movement data be exploited in order to improve the flow of traffic within a city? Movement data provides valuable information about routes and specific roads that people are likely to drive on. This allows us to pinpoint roads that occur in many routes and are thus sensitive to congestion. Redistributing some of the traffic to avoid unnecessary use of these roads could be a key factor in improving traffic flow. Many proposed approaches to combat congestion are either static or do… 

Figures from this paper

Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data
TLDR
This work presents a method to redistribute traffic through the introduction of externally imposed variable costs to each road segment, assuming that all drivers seek to drive the cheapest route, and proposes using a metaheuristic optimisation approach to minimise total travel times by optimising a set of road-specific variable cost parameters.

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